Management of treatment-refractory opioid use disorder: a case-series of a modified maintenance protocol
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Reaching the maintenance phase and reducing illicit substance use in those with severe opioid use disorder (OUD) remains a large clinical challenge. The use of intravenous opioid agonist treatment has become a last-line treatment for severe OUD. In this case series, we describe a novel form of this treatment using both long-acting buprenorphine and injectable hydromorphone. METHODS: Retrospective chart review of cases in specialized enhanced addiction treatment centers in Alberta, Canada of patients prescribed long-acting injectable buprenorphine and intravenous hydromorphone. RESULTS: We describe four cases of males ranging from 39 to 64 years of age with treatment-refractory opioid use disorder and multiple concurrent mental health and social issues who have experienced various periods of stability on long-acting injectable buprenorphine and along with intravenous hydromorphone or slow release oral morphine). DISCUSSION AND CONCLUSIONS: This case series presents a novel treatment combining buprenorphine extended-release injection with hydromorphone or slow release oral morphine for individuals with OUD who have not responded to standard opioid agonist treatment. The approach showed promise in managing withdrawal and cravings, potentially improving treatment retention. Further research is needed to evaluate long-term outcomes and optimize retention strategies to reduce opioid-related mortality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".